Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms

7 March 2017

Papers citing "Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms"

5 / 5 papers shown

Title
Contractivity and linear convergence in bilinear saddle-point problems: An operator-theoretic approach Colin Dirren Mattia Bianchi Panagiotis D. Grontas John Lygeros Florian Dorfler 81 0 0 18 Oct 2024
Stochastic Variance Reduction Methods for Saddle-Point Problems B. Palaniappan Francis R. Bach 109 212 0 20 May 2016
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives Aaron Defazio Francis R. Bach Simon Lacoste-Julien ODL 105 1,817 0 01 Jul 2014
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 140 738 0 19 Mar 2014
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization Shai Shalev-Shwartz Tong Zhang 112 1,031 0 10 Sep 2012